Wenckebach Activity
Of the 400,000 sudden cardiac deaths (SCD) that occur in North America annually, sixty percent occur without any recognized warning. However, even among individuals in the top decile of SCD risk, each year only a small percentage die due to SCD. Potential SCD victims are indistinguishable from the rest of high risk individuals by present methods of analysis. Furthermore, a sizable majority of SCD victims (60%) are not even in the top decile of risk. There is currently no known way of targeting aggressive therapies at the small percentage of high risk subjects who will be SCD victims within any given year.
SCD typically results from the interplay of three underlying factors: myocardial vulnerability, electrical instability, and neuroendocrine activation. However, any single factor may suffice to produce SCD.
Classically, risk factor analysis has been used to identify those at elevated risk of SCD. In recent years, to improve SCD risk assessment, researchers have introduced purportedly direct measures of cardiovascular instability and related these to the incidence of SCD. Since most of the proposed measures focus on only one of the many factors underlying SCD, the raw measures provide (statistically) marginal analyses and hence tend to be inadequate for diagnostic purposes.
To some extent, improper test marginality can be overcome through the proper choice of the measurement context. For example, a patient recovering from a recent myocardial infarction will most likely have a pathologically heterogeneous myocardial substrate. Exercising that patient will superimpose neuroendocrine activation. The electrical instability factor can then be studied.
Experiments on myocardial cells show that metabolically compromised tissue may respond to stimuli in a patterned manner. Such patterns in the electrocardiogram (ECG) should be directly related to electrical instability and may be more predictive of SCD than currently used non-invasive clinical ECG analysis methods. One form of patterned behavior is T-wave alternans, which has been studied as related to ventricular fibrillation. See, e.g., D. S. Rosenbaum et al., "Electrical Alternans and Vulnerability to Ventricular Arrhythmias," NEJM330(4): 1994, 235-241; R. L. Verrier and B. D. Nearing, "Electrophysiologic Basis for T Wave Alternans as an Index of Vulnerability to Ventricular Fibrillation," J Cardiovasc Physiol 5: 445-461, 1994; I. Watanabe et al., "Two Types of ST-T Alternans During Acute Myocardial Ischemia in the In-Situ Pig Heart," Circ 92(8): I-640, 1995; and D. L. Carson et al., "Characterization of unipolar waveform alternation in acutely ischaemic porcine myocardium." Cardiovasc Res 20: 521-527, 1986.
T-wave alternans appears in the body-surface ECG as a patterned beat-to-beat variation in the T-wave of ECG waveforms (FIG. 2A and G. H. Mudge, Jr., Manual of Electrocardography, Boston: Little, Brown, and Co., 1986). It involves voltage levels in the T-wave that switch between two values on alternate beats: high, low, high, low, and so on.
Intramyocardical Wenckebach activity is another form of patterned beat-to-beat variability, but can be more complex than altemans. The term "Atrio-ventricular (A-V) Wenckebach behavior" in cardiology commonly refers to a cardiac arrhythmia in which the conduction system fails to begin depolarization of the ventricles in a patterned manner (for example, failing on every other, or every third, beat). "Intramyocardial Wenckebach behavior" refers to repeating patterns of anomalous electrical activity within the ventricles, due to the patterned failure of (localized) regions of myocardium to depolarize. These regions are usually ischemic, compromised due to lack of an adequate oxygen supply.
In both traditional Wenckebach and intramyocardial Wenckebach anomalies, a repeating stimulus travels through the heart to metabolically (and thus electrically) compromised tissue. This tissue responds to each stimulus by depolarizing (and thus conducting the stimulus) until it becomes electrically fatigued. It then fails to respond to one or more stimuli until it recovers, at which time it responds to the next arriving stimulus. The behavior then repeats. This patterned activity can be specified by a code involving a pair of integers, M:N. The first integer gives the periodicity of the pattern, i.e., how many trigger events occur before the cycle begins afresh; the second integer tells how many times the electrically compromised tissue responds before it fatigues and fails. Thus, for example, a 4:3 Wenckebach pattern has a period of four beats with the tissue responding to three of the stimuli before it fails: respond, respond, respond, fail; respond, respond, respond, fail; . . . and so on. As a voltage phenomenon, T-wave alternans mimics 2:1 Wenckebach activity, but is localized to the T-wave. Hence, a technique that can detect 2:1 Wenckebach activity can also detect alternans.
Whereas the common form of atrial-ventricular (A-V) Wenckebach behavior is readily apparent on the body-surface ECG, intramyocardial Wenckebach manifests itself as small, anomalous, beat-to-beat electrical potential variations buried in much larger normal variations due to respiration and noise. Respiration modulates the ECG due to the physical movement of the heart (which rests on the diaphragm) relative to the recording electrodes; it affects conductivity within the thorax; it causes variations of heart size due to its effect on cardiac filling. Respiration is the largest source of interference. Noise sources include superimposed muscle tremor noise (EMG), gastric electrical activity (electrogastrogram, EGG), and electromagnetic interference (EMI). Detection of intramyocardial Wenckebach activity is further complicated when an affected region of the heart switches from one Wenckebach pattern to another and when multiple regions exhibit disparate, even competing, Wenckebach patterns.
Investigation of alternans activity (as it relates to susceptibility to sudden cardiac death) has been reported by two university groups: Massachusetts Institute of Technology (MIT) and Georgetown University. See, e.g., D.S. Rosenbaum et al., Supra.; U.S. Pat. No. 4,802,491; U.S. Pat. No. 5,148,812; and U.S. Pat. No. 5,437,285. In the MIT method, the incidence and extent of T-wave alternans was estimated by steps including formation of a spatial magnitude vector, identification of and aligning 128 T-waves each of 150 msec duration, and Fast Fourier transformation (FFT) of each epoch of the T-waves. The MIT group has a paper discussing the analysis for alternans while the patient is pedaling at one-third the heart rate (D. S. Rosenbaum et al., "Predicting Sudden Cardiac Death from T Wave Alternans of the Surface Electrocardiogram: Promises and Pitfalls," J Cardiovasc. Electrophysiology, 7: 1095-1111, November 1996). In U.S. Pat. No. 5,570,696, the MIT group describes a method for assessing myocardial electrical stability by increasing heart rate to provoke alternans behavior. In the Georgetown method, data were analyzed via the method of complex demodulation. The Georgetown group attributes T-wave alternation to the cellular monophasic action potential duration alternans phenomenon, not to gap junction failure (the mechanism originally put forward by the MIT group). Thus far, prior art techniques are not well suited to the detection of mixtures of Wenckebach activity.
Subspace Filtering Methodology
When modem signal analysts are required to extract a subtle signal from a timeseries corrupted by interference and noise, a useful technique is "subspace filtering." See, e.g., V. C. Klema and A. J. Laub, "The Singular Value Decomposition: Its Computation and Some Applications," IEEE Trans AC 25 (2): 164-176, 1980; A-J van der Veen et al., "Subspace-Based Signal Analysis Using Singular Value Decomposition," Proc IEEE 81 (9): 1277-1308, 1993.
In subspace filtering, a given timeseries is viewed as a vector in a multi-dimensional space. The technique seeks to determine a subspace that contains the signal part of the timeseries while being orthogonal to the interference and noise. The basis vectors of the full vector space, as well as its subspaces, are typically determined using linear algebra's well-known method of singular value decomposition (SVD). Expanded in terms of the basis vectors of the signal subspace, the signal emerges from the obscuring interference and noise.
Subspace filtering for compression of the electrocardiogram (ECG) data was investigated more than two decades ago. It was assumed that the basis vectors corresponding to the smallest singular values would correspond to noise. Recently, W. H. Hutson applied subspace filtering methods to ECG signals by suppressing interference to detect both altemans and late potentials. See W. H. Hutson, "High-Resolution Subspace Techniques for Cardiac Analysis," Proceedings of the Int. Conf On Sig Proc. Appl. and Tech. 1995: 230-238; and W. H. Hutson, U.S. Pat. No. 5,474,078.
U.S. patent application Ser. No. 08/722,351, Attorney Docket No. 10960792-1, commonly assigned with the assignee of the present application, discloses a subspace analysis technique applied to analyze Wenckebach activity. In that 08/722,351 application, which is incorporated by reference in its entirety herein, the respiratory interference subspace is oblique to the Wenckebach subspace and the noise subspace. A Wenckebach matrix is used to treat, in parallel, signals from the Wenckebach activity and the respiratory interference. What is needed is a technique that can analyze for Wenckebach activity, including altemans activity, without the necessity of using such an oblique treatment.